Error rate-based drift detection methods form the largest category of drift detection algorithms. The intuition behind this type of algorithms is to monitor fluctuations in the error rate as time passes. Once the change of the error rate is proven to be statistically significant, a drift alarm will be triggered. One of the top referenced this type of algorithms is the Drift Detection Method (DDM) [1]} which was the first algorithm to contain defined warning and drift levels for signalling concept drift. Many subsequent algorithms have adopted a similar implementation, e.g., Early Drift Detection Method (EDDM) [2]}, Heoffding's inequality-based Drift Detection Method (HDDM) [3]} and based on the Kolmogorov-Smirnov (KS) statistical test. In contrast to methods such as DDM, a two-time window-based drift detection algorithm called Adaptive Windowing (ADWIN) [4]} was proposed. In ADWIN, the size of the compared windows can be adjusted automatically.
